AI Specialist (Machine Learning)

The AI Specialist is responsible for architecting and operating an end-to-end data and AI platform, including a centralised data warehouse, ETL/ELT pipelines, ML systems, and LLM-based applications. The role involves building RAG architectures with vector databases and embedding pipelines, applying MLOps and LLMOps practices, and orchestrating data and AI workflows using n8n for production-scale deployment.

Key Responsibilities:

Data Platform & Engineering

• Design, build, and maintain a centralised data warehouse to support analytics, ML, and Large Language Model (LLM) workloads
• Develop and manage ETL/ELT pipelines to ingest, transform, and curate data from internal and external sources
• Ensure data quality, reliability, security, and governance across all data systems

Machine Learning & LLM Development

• Design, train, evaluate, deploy, and maintain machine learning models for predictive and analytical use cases
• Develop and integrate LLM solutions for enterprise and business applications
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured data
• Build and manage vector databases and embedding pipelines for semantic search and RAG systems

MLOps, LLMOps & Automation

• Deploy and maintain ML and LLM services in production environments
• Monitor model performance, data drift, prompt effectiveness, and retraining or re-indexing requirements
• Build and manage automated workflows using n8n to orchestrate data pipelines, ML processes, LLM inference, and system integrations

Integration, Collaboration & Governance

• Integrate AI, ML, and LLM solutions with applications via APIs and services
• Collaborate with stakeholders to translate business requirements into scalable AI-driven solutions
• Document system architecture, data pipelines, ML models, and LLM/RAG designs
• Ensure compliance with data privacy, security, and responsible AI practices

Requirements:

• Bachelors degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field
• At least 6 years of experience in data engineering, machine learning, or AI system development
• Hands-on experience deploying ML and LLM solutions in production environments.
• Experience building and deploying machine learning models using frameworks such as Scikit-learn, TensorFlow, or PyTorch
• Practical experience working with LLMs (e.g. OpenAI, Azure OpenAI, Anthropic, or open-source models)
• Experience designing or implementing RAG architectures
• Experience with workflow automation tools (e.g. n8n, Airflow, or similar)
• Proficiency in Python and SQL
• Experience with data warehouse technologies (e.g. BigQuery, Snowflake, Redshift, Azure Synapse)
• Understanding of data modelling, ETL/ELT processes, MLOps, and LLMOps concepts
• Hands-on experience with vector databases, embedding models, semantic search, and document chunking strategies
• Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel
• Experience with prompt engineering, evaluation, and guardrails
• Familiarity with APIs, microservices, and event-driven systems
• Experience with BI tools (Power BI, Tableau, Looker)
• Understanding of data governance, privacy regulations, and responsible AI principles